Mr. Pravin Dwiwedi of JP Morgan Chase

As enterprises have been adopting cloud-native data platforms, security has become both more critical and more complex. Analytical platforms, Generative AI systems, and Feature Engineering pipelines each carry unique vulnerabilities and pose a breach risk. Drawing on experience architecting enterprise-scale AI/ML platforms at JPMorgan Chase, this session delivers a practitioner's guide to securing all three platform types in the cloud. We will cover the threat landscape for each, from misconfigured data lakes and pipeline injection attacks to prompt injection in LLMs, RAG data leakage, and feature poisoning in ML pipelines. Attendees will learn how Zero Trust principles, data classification, dynamic masking, and access governance apply across these environments. The session also addresses AI-specific guardrails, securing RAG pipelines, and navigating key regulatory frameworks, including GDPR, PCI-DSS, and the EU AI Act. The talk concludes with a practical, platform-specific security checklist that can be applied immediately.
Key Topics: Threat modeling | Zero Trust | GenAI & RAG security | Feature store protection | AI governance & complianc

Biography:

Pravin Dwiwedi is a Senior Solution Architect specializing in AI/ML Platforms at JP Morgan Chase, where he leads the design and implementation of enterprise-scale data and artificial intelligence systems for Consumer Banking. With over 20 years of experience in data architecture and AI engineering, he brings deep expertise in building cloud-native, secure, and production-grade machine learning platforms at scale.

He has designed Databricks-based feature platforms supporting batch and online inference, cross-platform feature engineering frameworks spanning Data Lake, Snowflake, and Data Ponds, and responsible AI guardrail systems leveraging Retrieval Augmented Generation (RAG) capabilities.

His work spans the full spectrum of modern data and AI governance, from regulatory compliance frameworks aligned with GDPR, PCI-DSS, and the EU AI Act, to observability pipelines ensuring model lifecycle integrity in regulated financial environments.

Pravin holds a Master of Business Administration (MBA) and a Master of Computer Applications (MCA). He is a sought-after practitioner and speaker on cloud data security, GenAI platform engineering, and responsible AI at enterprise scale.

https://www.linkedin.com/in/pravindwiwedi/ 

Email:

Address:New Jersey, United States

Mr. Anil Malakar of AWS

Biography:

Anil Malakar is a Strategic Technical Advisor and Cloud & AI Solutions Architect, AWS, with over 22 years of experience in technology. He specializes in cloud computing, artificial intelligence, and solutions architecture, with deep expertise in generative AI implementations, serverless architectures for data migration and validation, and automated processes that drive operational efficiency. He has authored several publications on topics including generating actionable insights with generative AI, custom serverless architectures for data validation, effective API error handlers, and practical MVC approaches.

Email:

Address:New Jersey, United States